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Aalborg Universitet
Voltage Harmonics Monitoring in a Microgrid Based on Advanced Metering
Infrastructure (AMI)
Savaghebi, Mehdi; Guan, Yajuan; Quintero, Juan Carlos Vasquez; Guerrero, Josep M.;
Nielsen, Carsten
Published in:
Proceedings of the Seminario Anual de Automática, Electrónica Industrial e Instrumentación 2015 (SAAEI’15)
Publication date:
2015
Document Version
Early version, also known as pre-print
Link to publication from Aalborg University
Citation for published version (APA):
Firoozabadi, M. S., Guan, Y., Quintero, J. C. V., Guerrero, J. M., & Nielsen, C. (2015). Voltage Harmonics
Monitoring in a Microgrid Based on Advanced Metering Infrastructure (AMI). In Proceedings of the Seminario
Anual de Automática, Electrónica Industrial e Instrumentación 2015 (SAAEI’15).
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1
Voltage Harmonics Monitoring in a Microgrid
Based on Advanced Metering Infrastructure
(AMI)
Mehdi Savaghebi, Member, IEEE, Yajuan Guan, Student Member, IEEE, Juan C. Vasquez, Senior
Member, IEEE, Josep M. Guerrero, Fellow, IEEE, and Carsten Nielsen

Abstract—Smart meters are the main part of Advanced
Metering Infrastructure (AMI) and are usually able to provide
detailed information on customers’ energy consumptions, voltage
variations and interruptions. In addition, these meters are
potentially able to provide more information about power quality
(PQ) disturbances. This paper will address the monitoring of
voltage harmonics utilizing the features of smart meters and AMI
system. To do this, the first step is to select proper indices to
quantify the distortion. An important point which should be
considered in this regard is the limited processing power of smart
meters in comparison with PQ Analyzers (PQAs). Furthermore,
the indices are categorized as site and system indices. The site
indices measure the distortion at one metering section while the
system indices provide a PQ view in an area encompassing
multiple meters. In addition, the smart metering system which is
implemented in Intelligent Microgrid Lab of Aalborg University
(AAU) will be introduced.
Index Terms—Advanced Metering Infrastructure (AMI),
Power quality indices, Smart meter, Voltage harmonics.
A
I. INTRODUCTION
DVANCED Metering Infrastructure (AMI) consists of
smart meters and data storage, management and
communication systems. Smart meter (SM) is one of the most
important devices in smart grid (SG). The smart meter is an
advanced energy meter that collects information from the end
users’ load devices and measures the energy consumption of
the consumers as well as the generating power of the
distributed generation system. The SM provides real-time
information to the utility company and/or system operator.
Several sensors and control devices, supported by dedicated
communication infrastructure, are utilized in a smart meter
[1]-[3]. Fig. 1 shows the metering architectures comparison of
a conventional electrical meter and a smart meter.
On the other hand, proliferation of nonlinear loads in recent
years has increased the voltage harmonics distortion especially
in low-voltage distribution networks. Thus, power quality
(PQ) monitoring to ensure proper operation of the system is of
This work was supported by the Energy Technology Development and
Demonstration Program (EUDP) through the Sino-Danish project “Microgrid
Technology Research and Demonstration” (meter.et.aau.dk)
M. Savaghebi, Y. Guan, J. C. Vasquez and J. M. Guerrero are with the
Dept. Energy Tech., Aalborg University, Denmark (e-mail: {mes, ygu, juq,
joz}@et.aau.dk).
C. Nielsen is with Kamstrup A/S, Denmark (email: [email protected]).
great importance. In order to perform PQ surveys, it is
possible to apply fixed and/or portable PQ analyzers (PQAs).
PQAs are able to provide detailed data of various PQ
phenomena. However, installation of fixed PQAs necessitates
a large investment while portable ones can only provide the
PQ data during the monitoring period. In this regard, smart
meters can provide PQ monitoring among their other
capabilities [4]-[6]. PQ data recorded by these meters are not
as exact as PQAs recordings, but are provided continuously
and at approximately no additional cost. Furthermore, AMI
system abilities for data communication, processing and
management can be effectively applied in PQ applications.
Based on this, overall evaluation of PQ in an area
encompassing a large number of meters will be possible.
Thus, research for extending the use of smart meters for PQ
monitoring will present noticeable technical and economic
benefits.
II. SMART METERING SYSTEM IN AALBORG UNIVERSITY
(AAU) INTELLIGENT MICROGRID LAB
In order to implement the smart meter system, realize the 2way communication between Smart meters and data collector,
12 smart meters are installed in AAU Microgrid Lab. Some
tests have been done based on different series of smart meters
and different communication technologies.
A. Configuration
The Intelligent Microgrid laboratory in AAU is based on 6
workstations. Each workstation includes four DC-AC
converters, LCL-filters, ABB Motorized change-over switches
and two Kamstrup Smart-meters, as depicted in Fig. 2. Fig. 3
shows the configuration and electrical structure of each work
station. It is can be seen, the Kamstrup 351B industrial smart
meter is used to measure the total generating power of the
workstation (can be regarded as a DG system) and Kamstrup
382L residential smart meter measures one of the load
consumption (can be regarded as one building).
B. Communication
Smart Metering Systems are varied based on different
technology and design, but operate through a simple overall
process. Smart Meters collect data from the end consumers
and transmit this data information through the Local Area
Network (LAN) to the data collector. This transmission
2
communication technologies are employed by now: Optical
head and the Internet-based on TCP/IP. Some tests have been
done based on these to communication methods. There are
also some other communication technologies used for smart
meter system based smart grid, such as radio mess, power line
carrier (PLC), Zigbee, 3G, GPRS and so on. In smart grid
applications, there are different advantages and disadvantages
associated with them. The utilities choose the best technology
based on their business profits and real implementation [7].
process can be executed every 15 minutes or other slower
frequency based on the requirement of the data demand. After
that the collector retrieves the data and then transmits it. The
utility central collection points further processes the data by
using the Wide Area Network (WAN). Since the
communications path is two-way, signals or commands can be
sent directly to the meters, customer premise or distribution
device.
In MG Lab, two basic types of smart meter system
Customer
Collect Data manually
Conventional
Electrical Meter
Manual Billing
(a)
Customer
Smart Meter
Communication
Interface/protocol
Gateway
(b)
Fig. 1. Metering architectures comparison: (a) Conventional electrical meter, (b) Smart meter.
EMS
Fig. 2. Overview of intelligent MG Lab.
Fig. 3. Configuration of each setup.
Communication
Interface/protocol
Datebase and Management system
3
C. Kamstrup OMNIA System
OMNIA Suite is an AMI system which includes smart
meters, network communication, meter data management and
smart grid features tailored to the business of utilities today, as
shown in Fig. 4. In a near future, OMNIA suite will be
deployed in AAU Intelligent MG Lab, which will make the
Lab like a simulated smart grid platform. The general scheme
of this smart MG Lab is shown in Fig. 5. As depicted in the
figure, this Lab will include the OMNIPOWER smart meters,
OMNICON as data concentrator, utilidriver to integrate the
SM and the data management system (VisionAir) and energy
management system produced by AAU MG group. The Inhouse display system will also be included in this Lab.
This smart MG Lab can provide possibilities for substation
monitoring, voltage quality profiles like Total Harmonic
Distortion (THD) from each metering point. Thus, it helps to
give the grid owner a unique insight into the distribution
system. The enhanced intelligence in meters and concentrators
will provide quick responses as well as a high data frequency
from the low voltage grid. It can also provide load control
outputs for demand-side programs and load shedding ondemand.
Fig. 4. Configuration of Kamstrup OMNIA system
III. AMI-BASED VOLTAGE HARMONIC MONITORING
The first step toward voltage harmonic distortion in an AMI
system is to define proper quantifying indices. These indices
are studied in this section in two categories, namely, site
indices and system indices. The site indices measure the
distortion at one metering section (smart meter) while the
system indices provide a PQ view of a system (e.g. microgrid)
encompassing multiple meters. System indices are conceived
to be calculated in power quality application of central
software package of Meter Data Management (MDM) unit
which can be seen in Fig. 4.
A. Site Indices
THD is the most important harmonic index and can be
calculated as follows [8]:
50
THD 
V
h 2
V1
2
h
(1)
4
Fig. 5. Configuration of Intelligent Microgrid Lab combined with Kamstrup OMNIA system.
In IEC 61000-2-4 [9] and IEC 61000-3-6 [10], it is
suggested to consider up to 40th or 50th harmonic order for
THD calculation, but, when there is a low chance for
harmonic resonance, it is allowed to limit the calculation to
25th harmonic [11].
Some statistical harmonic indices proposed in different
standards can be summarized as following [12]:
 Daily 99% (or other percentile, e.g. 95%) of very short
term (calculated over 3 sec) individual harmonic indices
(per unit value of individual harmonics with rated
voltage base) or THD values. Daily 99% percentile is a
value that only 1% of very short term indices exceed that.
 Weekly 95% (or other percentile) of short term
(calculated over 10 min) individual harmonic indices or
THD values.
 Weekly peak value of individual or total harmonic
distortion values
It should be noted that very short term and short term
indices are calculated based on time aggregation process
which is explained in [12].
A problem which may arise in the case of harmonic
distortion is the excessive peak voltage which may result in
dielectric stress. Crest Factor (CF) is an index which
quantifies this phenomenon. CF is defined as the ratio of
voltage peak to rms values [13].
There are some other indices which measure the
interference in communication wires and telephone system
due to voltage and/or current harmonics. Among others,
Telephone Influence Factor (TIF), IT product and C message
index [13] can be mentioned. However, nowadays, these
indices are not practical as a result of effective shielding and
noise control methods.
B. System Indices
System indices can be directly calculated based on site
indices, directly. The main system indices are summarized
below [12]:
 System 95% THD (STHD95)
 System Average THD (SATHD)
 System Average Excessive Total Harmonic Distortion
Ratio Index (SAETHDRITHD*)
To define these indices, let us assume an electrical system
(e.g. a microgrid) with M monitoring sites (smart meters).
STHD95 is 95% percentile of a probability distribution. This
distribution is formed based on 95% percentiles of M
probability distributions [12], which each of them corresponds
to one smart meter. SATHD is similar to SATHD, but, it is
calculated by averaging individual 95% percentiles.
SAETHDRITHD* shows the number of reported THD indices
which exceed a threshold (THD*) in a specific time interval
like a day, week or month. Calculation of this index is
initiated by counting the number of site THD samples
(recorded hourly, for instance) which exceed THD*. Then,
this values is normalized over total THD samples in site s and
finally, the weighted sum of these normalized values is
extracted as follows [12]:
M
N

L s  THD * s 

s 1
 N Tot , s 
(2)
SAETHDRI THD * 
LT
In this equation, NTHD*s is the exceeding measured samples in
site s, NTot,s represents the total number of measurements, Ls is
the kVA supplied in site s and LT shows the total kVA
supplied in under-study system (microgrid).
5
IV. CONCLUSION
This paper has been dedicated to evaluation and monitoring
of power quality in microgrids which are equipped with AMI
which is also called smart metering system. Some details
regarding AMI implementation in AAU Intelligent Microgrid
Lab is provided, firstly and then, proper indices are proposed
to assess voltage harmonic, as a main PQ problem, in an AMIbased system. As the next steps, we are working on
monitoring of various PQ phenomena (e.g. voltage unbalance,
sag, swell, interruption) and finally, real implementation of a
PQ monitoring system in a smartly metered microgrid.
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